Beyond the Scale: Exploring the Factors Behind Obesity#

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Obesity has become a heavyweight issue in public health. Especially in the United States. Finding the exact causes of obesity is tough because many different factors contribute to it. It’s a complicated issue with many conflicting views.

One major factor is diet. Numerous studies show a strong link between poor nutritional habits and obesity. Diets high in processed foods, sugars, and unhealthy fats are common in many parts of the world, particularly in areas with high obesity rates. For instance, a study published in the Journal of Nutrition (https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-020-00963-2) found that individuals who consumed high amounts of fast food and sugary beverages were significantly more likely to be obese compared to those with healthier eating habits.

Conversely, another study published in the International Journal of Obesity highlights the role of genetic factors, indicating that individuals with certain genetic markers are more predisposed to obesity, regardless of their diet and physical activity levels. (https://www.hsph.harvard.edu/obesity-prevention-source/obesity-causes/genes-and-obesity/)

Political factors, such as government policies and public health initiatives, also impact obesity rates. Policies that promote healthy eating and physical activity can help reduce obesity, while those that do not address these issues may add to the problem.

As such, we researched the different correlations behind obesity, focusing on how diet quality, income levels, and voting behaviour influence obesity rates in the United States.

Nutritional Disparities#

It’s widely recognized that obesity is closely linked to poor dietary habits. Consuming unhealthy foods regularly can significantly impact one’s health. Financially, Americans are responsible for nearly half of the global expenditure on fast food, according to a report by Fortune Business Insights (https://www.fortunebusinessinsights.com/fast-food-market-106482). Additionally, during the period from 2013 to 2016, 36.6% of adults in the United States consumed fast food on any given day, as reported by the CDC (https://www.cdc.gov/nchs/products/databriefs/db322.htm). We plotted the obesity rates in the USA over the years, and our analysis compared the average nutritional values of typical meals to those of fast food to highlight the dietary factors contributing to these trends.

%run obesity_time.ipynb

The first graph shows a normalized comparison of nutritional content between fast food and typical meals, illustrating that fast food contains significantly higher levels of calories, fat, cholesterol, sodium, and sugar. These nutritional disparities contribute to adverse health effects, including obesity.

Part of the reason behind the high obesity rates is explained by the nutritional content of fast food. Research indicates that consuming ultra-processed foods leads to higher calorie intake and weight gain compared to minimally processed foods (https://pubmed.ncbi.nlm.nih.gov/31105044/) . Processed foods, like fast food, may be convenient and sometimes more affordable, but they often contain higher levels of sodium, sugar, and unhealthy fats, as shown in the bar graph above. These factors make fast food a significant contributor to the obesity epidemic.

Fast food and Obesity Density#

When we started researching the causes behind obesity, the first thing that came to mind was to plot the number of fast food restaurants against the adult obesity rate. Intuitively, one might expect that a higher density of fast food restaurants would correlate with higher obesity rates, given the association between fast food consumption and poor dietary habits. When you look at the world’s obesity ranking and fast food consumption, you could argue that there seems to be a correlation. (https://worldpopulationreview.com/country-rankings/fast-food-consumption-by-country, https://data.worldobesity.org/rankings/?age=a&sex=m)

However, as illustrated in the scatter plot, there appears to be no significant correlation between the number of fast food restaurants per 100,000 people and the adult obesity rate across U.S. states. The correlation coefficient is -0.16, indicating a very weak negative relationship, so we continued our search for more nuanced correlations.

Salary and Obesity#

Next, we explored the influence of salary on obesity across the United States. Given that individuals with higher incomes often have access to more expensive, healthier food options, as well as better education and healthcare, we were interested in investigating whether there is a correlation, and if so, how strong it is.

By graphing the income and obesity rates across all states in the United States, we observe a noticeable pattern: states with lower average incomes tend to have higher obesity rates, while states with higher incomes generally have lower obesity rates.

To further analyze this relationship, we created a scatter plot where each dot represents a state.

The downward-sloping line indicates a negative correlation between income and obesity, meaning that as the average income increases, the obesity rate tends to decrease. This negative correlation of -0.57, suggests a moderate inverse relationship between income and obesity levels across all states.

This graph reinforces our initial observation: higher income levels are associated with lower obesity rates. This could be due to various factors such as better access to nutritious food, higher levels of education, and improved healthcare in wealthier states.

Voting behaviour and adult obesity rate#

Following this, we also examined the relationship between voting behavior and obesity rates. By looking at voting patterns, we can see how different factors, like how much money people make and their political views, affect their health. This information is important because it can help us find the best ways to improve public health and address issues like obesity more effectively. The accompanying graph illustrates this analysis, where we compared the voting patterns in different states with their respective obesity rates.

%run obesity_voting.ipynb

The downward-sloping line indicates a negative correlation of -0.64, suggesting that states with a higher percentage of Democratic voters tend to have lower obesity rates. This correlation might reflect broader socioeconomic and cultural differences that influence both political preferences and public health outcomes.

Education and unemployment and adult obesity rate#

Education level are thought to have a big role as a determinant of health outcomes, including obesity rates. Higher education levels are often associated with better health literacy, healthier lifestyle choices, and greater access to resources that promote well-being. Mutually, higher unemployment rates can negatively impact health by reducing income and access to healthcare, increasing stress, and limiting opportunities for physical activity.

The bubble plot above illustrates the relationship between the percentage of adults with a bachelor’s degree or higher, the average unemployment rate from 2015 to 2019, and the adult obesity rate across different U.S. states. Each bubble represents a state, with the size of the bubble corresponding to the state’s average unemployment rate.

The plot reveals that states with higher percentages of adults holding a bachelor’s degree (or a higher level of education) generally have lower adult obesity rates. For instance, states like Colorado and Massachusetts, which have higher education levels, exhibit lower obesity rates compared to states like West Virginia and Mississippi, which have lower education levels.

Also, the size of the bubbles shows that states with higher unemployment rates often have higher obesity rates. This means that unemployment, along with lower education levels, increases the risk of obesity. The correlation coefficient of -0.65 backs this up, again showing a strong negative link between education and obesity rates.

Summary#

Obesity has plagued the United States for years and remains a significant health concern amongst many. This data analysis project tried to discover the causes of obesity by examining two main standpoints: socioeconomic factors and political influences.

From a socioeconomic perspective, the findings reveal that poor dietary habits, particularly high consumption of fast food rich in calories, fat, sodium, and sugar, are major contributors to obesity rates. Income levels also play a crucial role, with lower average incomes being associated with higher obesity rates. This reveals the importance of economic factors in public health, as wealthier individuals often have better access to healthier food options and healthcare.

From a political standpoint, voting behavior influences obesity rates, with states having a higher percentage of Democratic voters generally showing lower obesity rates. This reflects broader socioeconomic and cultural influences on public health. Additionally, education and unemployment further impact obesity rates. Higher education levels are linked to lower obesity rates due to better health literacy and access to resources, while higher unemployment rates are associated with higher obesity rates, highlighting the risk of obesity due to economic and educational disparities.

Although many potential explanations for the obesity crisis have been presented, further research and additional data are required to conclusively support any position.

References#

  1. González-Morales, R., Canto-Osorio, F., Stern, D., Sánchez-Romero, L. M., Torres-Ibarra, L., Hernández-López, R., Rivera-Paredez, B., Vidaña-Pérez, D., Ramírez-Palacios, P., Salmerón, J., Popkin, B. M., & Barrientos-Gutiérrez, T. (2020). Soft drink intake is associated with weight gain, regardless of physical activity levels: the health workers cohort study. ˜the œInternational Journal of Behavioural Nutrition and Physical Activity, 17(1). https://doi.org/10.1186/s12966-020-00963-2

  2. Genes are not destiny. (2016, April 11). Obesity Prevention Source. https://www.hsph.harvard.edu/obesity-prevention-source/obesity-causes/genes-and-obesity/

  3. Products - Data Briefs - Number 320 - September 2018. (n.d.). https://www.cdc.gov/nchs/products/databriefs/db322.htm

  4. Hall, K. D., Ayuketah, A., Brychta, R., Cai, H., Cassimatis, T., Chen, K. Y., Chung, S. T., Costa, E., Courville, A., Darcey, V., Fletcher, L. A., Forde, C. G., Gharib, A. M., Guo, J., Howard, R., Joseph, P. V., McGehee, S., Ouwerkerk, R., Raisinger, K., … Zhou, M. (2019). Ultra-Processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake. Cell Metabolism, 30(1), 67-77.e3. https://doi.org/10.1016/j.cmet.2019.05.008

  5. Fast food consumption by country 2024. (n.d.). https://worldpopulationreview.com/country-rankings/fast-food-consumption-by-country

  6. Ranking (% obesity by country). (n.d.). World Obesity Federation Global Obesity Observatory. https://data.worldobesity.org/rankings/?age=a&sex=m